• Introduction

• Materials and Methods

• Results

• Discussion

• Conclusion

• Data set of Rural People from Bangladesh with or without T1-Diabetes

• Contains 306 data points and 22 variables

• Classify T1 Diabetes people and explore important variables for T1 diabetes

• Obtain data set

• Data Wrangling

• EDA

• Analysis and Modeling

• LR model

• Shiny App

• Working collaboratively using RStudio Cloud and Github

# Load libraries
library("tidyverse")
# Load data
my_data_clean <- read_tsv(file = "/cloud/project/data/02_my_data_clean.tsv")
#mutate column 
my_data_clean <- my_data_clean %>%
  mutate(Age = case_when(Age == "greater then 15" ~ "> 15",
                         Age == "Less then 11" ~ "< 11",
                         Age == "Less then 15" ~"< 15",
                         Age == "Less then 5" ~ "< 5"),
         HbA1c = case_when(HbA1c == "Over 7.5%" ~"> 7.5%",
                           HbA1c == "Less then 7.5%" ~"< 7.5%"),
         BMI = round(BMI, 1))

# Wrangle data 
my_data_clean_aug <- my_data_clean %>%
  mutate(Dur_disease = str_extract(`Duration of disease`,"\\d+\\.?\\d*"),
  unit = str_replace(`Duration of disease`, Dur_disease,"")) %>%
  select(-`Duration of disease`)

# Converting duration to days for every value
my_data_clean_aug <- my_data_clean_aug %>%
  mutate(Dur_disease = as.numeric(Dur_disease)) %>%
  mutate(Dur_disease = case_when(unit == "d" ~ Dur_disease,
                                 unit == "w" ~ Dur_disease * 7,
                                 unit == "m" ~ Dur_disease * 30,
                                 unit == "y" ~ Dur_disease * 365),
         Dur_disease = replace_na(Dur_disease, 0)) %>%
  
  # We do not need the unit column anymore
  select(-unit) %>%
  
  # Separating "Other disease" column into three
  separate(`Other diease`,
           into = c("first_disease",
                    "second_disease",
                    "third_disease"),
           sep = ",")

• Limited by the data set: location, race and habitat of source data limit the global usability of the model

• Unique observation: Family history of diabetes does not impact the likelihood of diabetes

• The accuracy of our model can be increased with added parameters and data points

• Scope for cross platforming and integrated studies

• It was feasible to do data analysis and obtain biological insights about our data set

• We conclude that height and weight are important indicators of T1 diabetes

• We expected family history to be more important

• More descriptive data would have made it easier to conclude and test hypotheses